The advent of new health sensing technologies has
presented us with the opportunity to gain richer data from
patients undergoing clinical interventions. Such technologies are
particularly suited for applications requiring temporal accuracy.
The Wolf Motor Function Test (WMFT) is one such application.
This assessment is an instrument used to determine functional
ability of the paretic and non-paretic limbs in individuals poststroke
. It consists of 17 tasks, 15 of which are scored according
to both time and a functional ability scale. We propose a
technique that uses wearable sensors and performance sensors
to estimate the timing of seven of these tasks. We have developed
a sensing framework and an algorithm to automatically detect
total movement time. We have validated the system’s accuracy
on the seven selected WMFT tasks. We also suggest how this
framework can be adapted to the remaining tasks.